Tardigrade: leveraging lightweight virtual machines to easily and efficiently construct fault-tolerant services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many services need to survive machine failures, but designing and deploying fault-tolerant services can be difficult and error-prone. In this work, we present Tardigrade, a system that deploys an existing, unmodified binary as a fault-tolerant service. Tardigrade replicates the service on several machines so that it continues running even when some of them fail. Yet, it keeps the service states synchronized so clients see strongly consistent results. To achieve this efficiently, we use lightweight virtual machine replication. A lightweight virtual machine is a process sandboxed so that its external dependencies are completely encapsulated, enabling it to be migrated across machines. To let unmodified binaries run within such a sandbox, the sandbox also contains a library OS providing the expected API. We evaluate Tardigrade's performance and demonstrate its applicability to a variety of services, showing that it can convert these services into fault-tolerant ones transparently and efficiently.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it